602 lines
27 KiB
Python
602 lines
27 KiB
Python
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# coding=utf-8
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# Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch MiniMax model."""
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from typing import Optional
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import torch
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import torch.nn.functional as F
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from torch import nn
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...configuration_utils import layer_type_validation
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from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import MoeModelOutputWithPast
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, logging
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from ...utils.generic import OutputRecorder
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from ..mixtral.configuration_mixtral import MixtralConfig
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from ..mixtral.modeling_mixtral import (
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MixtralAttention,
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MixtralDecoderLayer,
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MixtralForCausalLM,
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MixtralForQuestionAnswering,
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MixtralForSequenceClassification,
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MixtralForTokenClassification,
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MixtralModel,
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MixtralPreTrainedModel,
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MixtralRMSNorm,
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MixtralSparseMoeBlock,
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)
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logger = logging.get_logger(__name__)
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class MiniMaxConfig(MixtralConfig):
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r"""
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This is the configuration class to store the configuration of a [`MiniMaxModel`]. It is used to instantiate an
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MiniMax model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the MiniMax.
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[MiniMaxAI/MiniMax-Text-01-hf](https://huggingface.co/MiniMaxAI/MiniMax-Text-01-hf)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the MiniMax model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MiniMaxModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 14336):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
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head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
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The attention head dimension.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
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The maximum sequence length that this model might ever be used with. MiniMax's sliding window attention
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allows sequence of up to 4096*32 tokens.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*):
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The id of the padding token.
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bos_token_id (`int`, *optional*, defaults to 1):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 2):
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The id of the "end-of-sequence" token.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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rope_theta (`float`, *optional*, defaults to 1000000.0):
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The base period of the RoPE embeddings.
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sliding_window (`int`, *optional*):
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Sliding window attention window size. If not specified, will default to `4096`.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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num_experts_per_tok (`int`, *optional*, defaults to 2):
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The number of experts to route per-token, can be also interpreted as the `top-k` routing
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parameter
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num_local_experts (`int`, *optional*, defaults to 8):
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Number of experts per Sparse MLP layer.
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output_router_logits (`bool`, *optional*, defaults to `False`):
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Whether or not the router logits should be returned by the model. Enabeling this will also
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allow the model to output the auxiliary loss. See [here]() for more details
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router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
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The aux loss factor for the total loss.
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router_jitter_noise (`float`, *optional*, defaults to 0.0):
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Amount of noise to add to the router.
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layer_types (`list`, *optional*):
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Attention pattern for each layer.
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block_size (`int`, *optional*, defaults to 256):
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The length of each attention block, determining how queries, keys, and values
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are grouped and processed for intra- and inter-block attention.
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full_attn_alpha_factor (`float`, *optional*, defaults to 1):
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Weight for residual value in residual connection after normal attention.
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full_attn_beta_factor (`float`, *optional*, defaults to 1):
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Weight for hidden state value in residual connection after normal attention.
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linear_attn_alpha_factor (`float`, *optional*, defaults to 1):
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Weight for residual value in residual connection after lightning attention.
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linear_attn_beta_factor (`float`, *optional*, defaults to 1):
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Weight for hidden state value in residual connection after lightning attention.
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mlp_alpha_factor (`float`, *optional*, defaults to 1):
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Weight for residual value in residual connection after MLP.
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mlp_beta_factor (`float`, *optional*, defaults to 1):
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Weight for hidden state value in residual connection after MLP.
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```python
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>>> from transformers import MiniMaxModel, MiniMaxConfig
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>>> # Initializing a MiniMax style configuration
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>>> configuration = MiniMaxConfig()
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>>> # Initializing a model from the MiniMax style configuration
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>>> model = MiniMaxModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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def __init__(
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self,
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layer_types=None,
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block_size=256,
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full_attn_alpha_factor=1,
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full_attn_beta_factor=1,
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linear_attn_alpha_factor=1,
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linear_attn_beta_factor=1,
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mlp_alpha_factor=1,
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mlp_beta_factor=1,
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**super_kwargs,
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):
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super().__init__(**super_kwargs)
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self.layer_types = layer_types
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self.block_size = block_size
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self.full_attn_alpha_factor = full_attn_alpha_factor
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self.full_attn_beta_factor = full_attn_beta_factor
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self.linear_attn_alpha_factor = linear_attn_alpha_factor
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self.linear_attn_beta_factor = linear_attn_beta_factor
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self.mlp_alpha_factor = mlp_alpha_factor
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self.mlp_beta_factor = mlp_beta_factor
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if self.layer_types is None:
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self.layer_types = [
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"full_attention" if bool((i + 1) % 2) else "linear_attention" for i in range(self.num_hidden_layers)
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]
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layer_type_validation(self.layer_types)
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class MiniMaxRMSNorm(MixtralRMSNorm):
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pass
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class MiniMaxCache(DynamicCache):
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def __init__(self):
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super().__init__()
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self.linear_cache: list[torch.Tensor] = []
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def set_linear_cache(self, layer_idx, linear_cache):
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# There may be skipped layers, fill them with empty lists
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for _ in range(len(self.linear_cache), layer_idx + 1):
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self.linear_cache.append([])
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self.linear_cache[layer_idx] = linear_cache
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def get_linear_cache(self, layer_idx: int):
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if layer_idx < len(self):
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return self.linear_cache[layer_idx]
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return None
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def __len__(self):
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return max(super().__len__(), len(self.linear_cache))
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def __getitem__(self, layer_idx: int):
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if layer_idx < len(self.linear_cache) and self.linear_cache[layer_idx] != []:
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return (self.linear_cache[layer_idx],)
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return super().__getitem__(layer_idx)
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def __iter__(self):
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for layer_idx in range(len(self)):
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yield self[layer_idx]
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def batch_repeat_interleave(self, repeats: int):
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for layer_idx in range(len(self)):
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if self.linear_cache[layer_idx] != []:
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self.linear_cache[layer_idx] = self.linear_cache[layer_idx].repeat_interleave(repeats, dim=0)
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else:
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self.layers[layer_idx].batch_repeat_interleave(repeats)
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def batch_select_indices(self, indices: torch.Tensor):
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for layer_idx in range(len(self)):
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if self.linear_cache[layer_idx] != []:
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self.linear_cache[layer_idx] = self.linear_cache[layer_idx][indices, ...]
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else:
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self.layers[layer_idx].batch_select_indices(indices)
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def crop(self, max_length: int):
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raise RuntimeError("MiniMaxCache doesnot support `crop` method")
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class MiniMaxLightningAttention(nn.Module):
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def __init__(self, config: MiniMaxConfig, layer_idx: int):
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super().__init__()
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
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self.num_attention_heads = config.num_attention_heads
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self.num_hidden_layers = config.num_hidden_layers
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self.block_size = config.block_size
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self.act_fn = ACT2FN[config.hidden_act]
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self.norm = MiniMaxRMSNorm(self.head_dim * self.num_attention_heads)
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self.qkv_proj = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim * 3, bias=False)
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self.out_proj = nn.Linear(self.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
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self.output_gate = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim, bias=False)
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slope_rate = self.get_slope_rate()
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query_decay, key_decay, diagonal_decay = self.decay_factors(slope_rate)
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self.register_buffer("slope_rate", slope_rate)
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self.register_buffer("query_decay", query_decay)
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self.register_buffer("key_decay", key_decay)
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self.register_buffer("diagonal_decay", diagonal_decay)
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def get_slope_rate(self):
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base = 1 / (2 ** (8 / self.num_attention_heads))
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exponent = torch.arange(self.num_attention_heads) + 1
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factor = 1 - self.layer_idx / (self.num_hidden_layers - 1 + 1e-5) + 1e-5
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rate = base**exponent
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rate = rate * factor
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rate = rate[:, None, None]
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return rate
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def decay_factors(self, slope_rate):
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block_size_range = torch.arange(self.block_size) + 1
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query_decay = torch.exp(-slope_rate * block_size_range[:, None])
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key_decay = torch.exp(-slope_rate * (self.block_size - block_size_range[:, None]))
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diagonal_decay = block_size_range[:, None] - block_size_range[None, :]
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diagonal_decay = diagonal_decay[None, None, :, :]
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diagonal_decay = slope_rate * diagonal_decay
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diagonal_decay = torch.where(diagonal_decay >= 0, -diagonal_decay, float("-inf"))
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diagonal_decay = torch.exp(diagonal_decay)
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return query_decay, key_decay, diagonal_decay
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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batch_size, seq_len, hidden_size = hidden_states.shape
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num_blocks = (seq_len + self.block_size - 1) // self.block_size
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qkv_states = self.act_fn(self.qkv_proj(hidden_states))
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qkv_states = qkv_states.reshape(batch_size, seq_len, self.num_attention_heads, 3 * self.head_dim)
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query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=3)
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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# calculated (K.T @ V) and saved as cache
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attn_weights_inter = None
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if past_key_value is not None:
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attn_weights_inter = past_key_value.get_linear_cache(self.layer_idx)
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if attn_weights_inter is None:
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attn_weights_inter = torch.zeros(batch_size, self.num_attention_heads, self.head_dim, self.head_dim).to(
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value_states
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)
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# apply attention_mask
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if attention_mask is not None:
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attention_mask = attention_mask.to(dtype=torch.bool) # Ensure it's a boolean tensor
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value_states = value_states.masked_fill(~attention_mask.unsqueeze(1).unsqueeze(-1), 0)
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attn_output = []
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for i in range(num_blocks):
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start_idx = i * self.block_size
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end_idx = min(start_idx + self.block_size, seq_len)
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current_block_size = end_idx - start_idx
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current_query_states = query_states[:, :, start_idx:end_idx]
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current_key_states = key_states[:, :, start_idx:end_idx]
|
||
|
|
current_value_states = value_states[:, :, start_idx:end_idx]
|
||
|
|
|
||
|
|
current_query_decay = self.query_decay[:, :current_block_size]
|
||
|
|
current_key_decay = self.key_decay[:, -current_block_size:]
|
||
|
|
current_diagonal_decay = self.diagonal_decay[:, :, :current_block_size, :current_block_size]
|
||
|
|
block_decay = torch.exp(-self.slope_rate * current_block_size)
|
||
|
|
|
||
|
|
# intra: ( Q @ K.T ) @ V -> QK * V
|
||
|
|
attn_weights_intra = torch.matmul(current_query_states, current_key_states.transpose(-1, -2))
|
||
|
|
attn_output_intra = torch.matmul(attn_weights_intra * current_diagonal_decay, current_value_states)
|
||
|
|
|
||
|
|
# inter: Q @ ( K.T @ V ) -> Q * KV
|
||
|
|
attn_output_inter = torch.matmul(current_query_states * current_query_decay, attn_weights_inter)
|
||
|
|
|
||
|
|
# final attention output
|
||
|
|
current_attn_output = attn_output_inter + attn_output_intra
|
||
|
|
attn_output.append(current_attn_output)
|
||
|
|
|
||
|
|
# cacluate attn_weights_inter for next block or cache
|
||
|
|
next_attn_weights_inter = torch.matmul(
|
||
|
|
(current_key_states * current_key_decay).transpose(-1, -2), current_value_states
|
||
|
|
)
|
||
|
|
attn_weights_inter = attn_weights_inter * block_decay + next_attn_weights_inter
|
||
|
|
|
||
|
|
else:
|
||
|
|
ratio = torch.exp(-self.slope_rate)
|
||
|
|
attn_output = []
|
||
|
|
for i in range(seq_len):
|
||
|
|
current_query_states = query_states[:, :, i : i + 1]
|
||
|
|
current_key_states = key_states[:, :, i : i + 1]
|
||
|
|
current_value_states = value_states[:, :, i : i + 1]
|
||
|
|
|
||
|
|
current_attn_weights_inter = torch.matmul(current_key_states.transpose(-1, -2), current_value_states)
|
||
|
|
attn_weights_inter = ratio * attn_weights_inter + current_attn_weights_inter
|
||
|
|
current_attn_output = torch.matmul(current_query_states, attn_weights_inter)
|
||
|
|
|
||
|
|
attn_output.append(current_attn_output)
|
||
|
|
|
||
|
|
# concatenate attention outputs over all blocks
|
||
|
|
attn_output = torch.cat(attn_output, dim=-2)
|
||
|
|
|
||
|
|
# final output projection
|
||
|
|
attn_output = attn_output.transpose(1, 2)
|
||
|
|
attn_output = attn_output.reshape(batch_size, seq_len, self.num_attention_heads * self.head_dim)
|
||
|
|
attn_output = self.norm(attn_output)
|
||
|
|
attn_output = F.sigmoid(self.output_gate(hidden_states)) * attn_output
|
||
|
|
attn_output = self.out_proj(attn_output)
|
||
|
|
|
||
|
|
# update cache
|
||
|
|
if past_key_value is not None:
|
||
|
|
past_key_value.set_linear_cache(self.layer_idx, attn_weights_inter)
|
||
|
|
|
||
|
|
return attn_output, attn_weights_inter
|
||
|
|
|
||
|
|
|
||
|
|
class MiniMaxAttention(MixtralAttention):
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
class MiniMaxSparseMoeBlock(MixtralSparseMoeBlock):
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
class MiniMaxDecoderLayer(MixtralDecoderLayer, GradientCheckpointingLayer):
|
||
|
|
def __init__(self, config: MiniMaxConfig, layer_idx: int):
|
||
|
|
super().__init__(config, layer_idx)
|
||
|
|
|
||
|
|
self.layer_idx = layer_idx
|
||
|
|
self.layer_type = config.layer_types[layer_idx]
|
||
|
|
self.mlp_alpha_factor = config.mlp_alpha_factor
|
||
|
|
self.mlp_beta_factor = config.mlp_beta_factor
|
||
|
|
|
||
|
|
if self.layer_type == "linear_attention":
|
||
|
|
self.self_attn = MiniMaxLightningAttention(config, layer_idx)
|
||
|
|
self.attn_alpha_factor = config.linear_attn_alpha_factor
|
||
|
|
self.attn_beta_factor = config.linear_attn_beta_factor
|
||
|
|
else:
|
||
|
|
self.self_attn = MiniMaxAttention(config, layer_idx)
|
||
|
|
self.attn_alpha_factor = config.full_attn_alpha_factor
|
||
|
|
self.attn_beta_factor = config.full_attn_beta_factor
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hidden_states: torch.Tensor,
|
||
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
|
past_key_value: Optional[tuple[torch.Tensor]] = None,
|
||
|
|
output_attentions: Optional[bool] = False,
|
||
|
|
output_router_logits: Optional[bool] = False,
|
||
|
|
use_cache: Optional[bool] = False,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
||
|
|
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
||
|
|
position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`):
|
||
|
|
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
|
||
|
|
with `head_dim` being the embedding dimension of each attention head.
|
||
|
|
attention_mask (`torch.Tensor`, *optional*): attention mask of size
|
||
|
|
`(batch, sequence_length)` where padding elements are indicated by 0.
|
||
|
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
||
|
|
output_attentions (`bool`, *optional*):
|
||
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||
|
|
returned tensors for more detail.
|
||
|
|
output_router_logits (`bool`, *optional*):
|
||
|
|
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
|
||
|
|
should not be returned during inference.
|
||
|
|
use_cache (`bool`, *optional*):
|
||
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
||
|
|
(see `past_key_values`).
|
||
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
||
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
||
|
|
kwargs (`dict`, *optional*):
|
||
|
|
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
||
|
|
into the model
|
||
|
|
"""
|
||
|
|
|
||
|
|
hidden_states = self.input_layernorm(hidden_states)
|
||
|
|
residual = hidden_states
|
||
|
|
|
||
|
|
# Self Attention
|
||
|
|
hidden_states, _ = self.self_attn(
|
||
|
|
hidden_states=hidden_states,
|
||
|
|
position_embeddings=position_embeddings,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
past_key_value=past_key_value,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
use_cache=use_cache,
|
||
|
|
cache_position=cache_position,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
hidden_states = residual * self.attn_alpha_factor + hidden_states * self.attn_beta_factor
|
||
|
|
|
||
|
|
# Fully Connected
|
||
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
||
|
|
residual = hidden_states
|
||
|
|
hidden_states, _ = self.block_sparse_moe(hidden_states)
|
||
|
|
hidden_states = residual * self.mlp_alpha_factor + hidden_states * self.mlp_beta_factor
|
||
|
|
|
||
|
|
return hidden_states
|
||
|
|
|
||
|
|
|
||
|
|
class MiniMaxPreTrainedModel(MixtralPreTrainedModel):
|
||
|
|
_can_compile_fullgraph = False
|
||
|
|
_can_record_outputs = {
|
||
|
|
"router_logits": OutputRecorder(MiniMaxSparseMoeBlock, index=1),
|
||
|
|
"hidden_states": MiniMaxDecoderLayer,
|
||
|
|
"attentions": [MiniMaxAttention, MiniMaxLightningAttention],
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
class MiniMaxModel(MixtralModel):
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
input_ids: torch.LongTensor = None,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
|
past_key_values: Optional[MiniMaxCache] = None,
|
||
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
|
|
use_cache: Optional[bool] = None,
|
||
|
|
output_attentions: Optional[bool] = None,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
**kwargs: Unpack[TransformersKwargs],
|
||
|
|
) -> MoeModelOutputWithPast:
|
||
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
||
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||
|
|
|
||
|
|
if use_cache and past_key_values is None:
|
||
|
|
past_key_values = MiniMaxCache()
|
||
|
|
elif use_cache and not isinstance(past_key_values, MiniMaxCache):
|
||
|
|
raise ValueError(
|
||
|
|
f"MiniMax uses cache of its own and is not compatible with `past_key_values` of type {type(past_key_values)}."
|
||
|
|
)
|
||
|
|
|
||
|
|
if inputs_embeds is None:
|
||
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
||
|
|
|
||
|
|
if cache_position is None:
|
||
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||
|
|
cache_position = torch.arange(
|
||
|
|
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
||
|
|
)
|
||
|
|
if position_ids is None:
|
||
|
|
position_ids = cache_position.unsqueeze(0)
|
||
|
|
|
||
|
|
mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
|
||
|
|
causal_mask = mask_function(
|
||
|
|
config=self.config,
|
||
|
|
input_embeds=inputs_embeds,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
cache_position=cache_position,
|
||
|
|
past_key_values=past_key_values,
|
||
|
|
position_ids=position_ids,
|
||
|
|
)
|
||
|
|
|
||
|
|
hidden_states = inputs_embeds
|
||
|
|
|
||
|
|
# create position embeddings to be shared across the decoder layers
|
||
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
||
|
|
|
||
|
|
for decoder_layer in self.layers:
|
||
|
|
if decoder_layer.layer_type == "full_attention":
|
||
|
|
input_attention_mask = causal_mask
|
||
|
|
else:
|
||
|
|
# lightning attention uses original attention_mask, and uses it only for the first step
|
||
|
|
input_attention_mask = attention_mask
|
||
|
|
|
||
|
|
hidden_states = decoder_layer(
|
||
|
|
hidden_states,
|
||
|
|
position_embeddings=position_embeddings,
|
||
|
|
attention_mask=input_attention_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
past_key_value=past_key_values,
|
||
|
|
use_cache=use_cache,
|
||
|
|
cache_position=cache_position,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
hidden_states = self.norm(hidden_states)
|
||
|
|
|
||
|
|
return MoeModelOutputWithPast(
|
||
|
|
last_hidden_state=hidden_states,
|
||
|
|
past_key_values=past_key_values,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class MiniMaxForCausalLM(MixtralForCausalLM):
|
||
|
|
def forward(self, **super_kwargs):
|
||
|
|
r"""
|
||
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import AutoTokenizer, MiniMaxForCausalLM
|
||
|
|
|
||
|
|
>>> model = MiniMaxForCausalLM.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
|
||
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
|
||
|
|
|
||
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
||
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||
|
|
|
||
|
|
>>> # Generate
|
||
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
||
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||
|
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
||
|
|
```"""
|
||
|
|
return super().forward(**super_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
class MiniMaxForSequenceClassification(MixtralForSequenceClassification):
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
class MiniMaxForTokenClassification(MixtralForTokenClassification):
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
class MiniMaxForQuestionAnswering(MixtralForQuestionAnswering):
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
__all__ = [
|
||
|
|
"MiniMaxConfig",
|
||
|
|
"MiniMaxPreTrainedModel",
|
||
|
|
"MiniMaxModel",
|
||
|
|
"MiniMaxForCausalLM",
|
||
|
|
"MiniMaxForSequenceClassification",
|
||
|
|
"MiniMaxForTokenClassification",
|
||
|
|
"MiniMaxForQuestionAnswering",
|
||
|
|
]
|